Predicting 90 day acute heart failure readmission and death using machine learning-supported decision analysis.

ABSTRACT: Readmission or death soon after heart failure (HF) admission is a significant problem. Traditional analyses for predicting such events often fail to consider the gamut of characteristics that may contribute- tending to focus on 30-day outcomes even though the window of increased vulnerability may last up to 90 days. Risk assessments incorporating machine learning (ML) methods may be better suited than traditional statistical analyses alone to sort through multitude of data in the electronic health record (EHR) and identify patients at higher risk. HYPOTHESIS: ML-based decision analysis may better identify patients at increased risk for 90-day acute HF readmission or death after incident HF admission. METHODS AND RESULTS: Among 3189 patients who underwent index HF hospitalization, 15.2% exp

SUBMITTER: Sarijaloo F 

PROVIDER: S-EPMC7852168 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature